The underlying causes for the shortage of nurses and how to rectify it: a comparison between Canada and Israel
Bibliographic record
Abstract
The shortage of registered nurses (RNs) is a challenging situation in many developed and developing countries, and this phenomenon is expected to exacerbate in the coming years, given the rise in life expectancy at birth. Many scholars emphasize the importance of RNs in achieving quality care, preventing complications, and achieving desired medical and health outcomes. Therefore, the shortage of nurses has a direct impact on the health of the population. This study conducts a comparison between the shortage of nurses in Canada and Israel. The study found many similarities in the causes of this shortage, yet there are differences in the assignment of nurses as well as in the recruitment of foreign nurses from abroad. Further, the number of new graduates who joined the health system in Israel and Canada in recent years was constant and stable. Looking at the trends in the employment of nurses in recent years, we learn about an increase in the number of nurses employed and a flat line over the years in the ratio of nurses per thousand inhabitants in both Israel and Canada. Additional reasons for the shortage of RNs lie in the slight increase in the number of students graduating from nursing schools in both Israel and Canada. Finally, both countries need to develop the training of RNs as a result of the increasing medical complexity of the patients, which requires professional nursing intervention in hospitalization and in the community. The article also discusses the issue of increasing the supply of nurses through retention and migration policies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".